Controlling Reading Ease with Gaze-Guided Text Generation

TL;DR

This paper presents a novel method that uses gaze prediction to generate texts with adjustable reading difficulty, aiding accessibility and personalized education.

Contribution

It introduces a gaze-guided text generation model that controls reading ease by steering language outputs based on predicted eye movement patterns.

Findings

Effective at adjusting reading difficulty as shown by eye-tracking data

Texts can be made easier or harder to read through the method

Changes primarily affect lexical processing features

Abstract

The way our eyes move while reading can tell us about the cognitive effort required to process the text. In the present study, we use this fact to generate texts with controllable reading ease. Our method employs a model that predicts human gaze patterns to steer language model outputs towards eliciting certain reading behaviors. We evaluate the approach in an eye-tracking experiment with native and non-native speakers of English. The results demonstrate that the method is effective at making the generated texts easier or harder to read, measured both in terms of reading times and perceived difficulty of the texts. A statistical analysis reveals that the changes in reading behavior are mostly due to features that affect lexical processing. Possible applications of our approach include text simplification for information accessibility and generation of personalized educational material for language learning.